Devices for range estimation in battery powered vehicles

ABSTRACT

Systems, methods, devices, and models for range estimation and analysis in battery powered vehicles are described. Energy consumption over vehicle trips is collected, to evaluate weighting factors for a weighted sum. The weighted sum is evaluated based on determined weighting factors and expected trip data, to determine an energy consumption of a trip or trips of a vehicle. Determined energy consumption for trips is used for evaluating suitability of the vehicle for performing the trips.

PRIOR APPLICATION DATA

The present patent application claims priority to US Provisional Pat.Application No. 63/323,210, filed Mar. 24, 2022, titled “Systems,Methods, Devices, and Models for Range Estimation in Battery-PoweredVehicles”.

TECHNICAL FIELD

The present disclosure generally relates to systems, devices, methods,and models for determining range of battery-powered vehicles, and inparticular relates to determining range based on energy consumption orefficiency of battery-powered vehicles.

BACKGROUND

Battery-powered vehicles (e.g. all-electric vehicles, hybrid electricvehicles, etc.) are a convenient and environmentally friendly means oftransportation. A battery-powered vehicle includes at least one battery,which can be charged from an external power source. A battery-poweredvehicle can alternately be called an “electric vehicle”. It is desirableto be able to accurately predict a distance a battery-powered vehicle iscapable of driving (range). This is especially true in regions wherecharging infrastructure is limited, such that unexpected or unplannedcharging of a battery-powered vehicle can be difficult. Further, evenfor vehicles with alternate non-battery power sources (e.g. hybridelectric vehicles, plug in hybrid vehicles), it is desirable to be ableto accurately predict a range of such vehicles on battery power, formore efficient operation (e.g. by minimization of operation onnon-battery power).

SUMMARY

According to a broad aspect, the present disclosure describes a methodfor estimating energy consumption by a vehicle for a trip by thevehicle, based on trip data representative of the trip, the methodcomprising: identifying a distance of travel for the trip based on aplurality of geographic positions represented in the trip data;identifying a duration of the trip based on a plurality of timestamps inthe trip data; identifying an ambient temperature of an environment ofthe vehicle for the trip based on temperature data; identifying speed ofthe vehicle for the trip based on the trip data; determining energyconsumption by the vehicle for the trip as a weighted sum of: energyloss due to vehicle friction, based on the identified distance oftravel; a total time of the trip, based on the identified total time ofthe trip; energy loss due to temperature control of the vehicle, basedon a difference between the identified ambient temperature of thevehicle and an optimal temperature, for the duration of the trip; andenergy loss due to air resistance, based on the identified speed of thevehicle; and outputting the determined energy consumption by the vehiclefor the trip.

Identifying the distance of travel may comprise: determining respectivedistances between sequential geographic positions represented in thetrip data; summing each of the determined respective distances.

Identifying a duration of the trip may comprise determining a differencebetween a first timestamp corresponding to a beginning of the trip and asecond timestamp corresponding to an end of the trip.

The temperature data may be separate from the trip data; and identifyingan ambient temperature of an environment of the vehicle may compriseidentifying, from the temperature data, ambient temperature during thetrip for a geographical region corresponding to a geographical regionwhere the trip occurred.

The temperature data may be included in the trip data, and thetemperature data may include a plurality of indications of temperatureof an environment of the vehicle during the trip; the method may furthercomprise determining an average ambient temperature of an environment ofthe vehicle over the trip, by averaging temperature indicated in theplurality of indications of temperature of an environment of the vehicleduring the trip; and energy loss due to temperature control of thevehicle may be based on a difference between the determined averageambient temperature and an optimal temperature, for the duration of thetrip.

The temperature data may be included in the trip data, and thetemperature data may include a plurality of indications of temperatureof an environment of the vehicle during the trip; and energy loss due totemperature control of the vehicle may be based on a respectivedifference between each indication of ambient temperature of the vehicleand an optimal temperature, over the duration of the trip.

Identifying a speed of the vehicle may comprise identifying speed of thevehicle for a plurality of segments of the trip; and energy loss due toair resistance for the trip may be determined as a summation of energyloss due to air resistance for each segment of the trip, based on theidentified speed of the vehicle for each segment of the trip.Identifying speed of the vehicle for a plurality of segments of the tripmay comprise: identifying each segment of the trip as being betweensequential geographic positions indicated in the trip data withcorresponding timestamps; and identifying speed of the vehicle for eachsegment of the trip by determining, for each segment of the trip,distance of the segment as distance between sequential geographicpositions corresponding to the respective segment, duration of thesegment as difference in time between timestamps corresponding to abeginning of the segment and an end of the segment, and dividing therespective distance by the respective duration for the segment.

The weighted sum may further include energy consumed to impart kineticenergy to the vehicle, based on change in speed of the vehicle duringthe trip. The weighted sum may further include energy recovered by aregenerative braking system of the vehicle, based on change in speed ofthe vehicle during the trip.

The method may further comprise: collecting, by a telematic monitoringdevice positioned at the vehicle, the trip data.

The method may further comprise: simulating the trip by the vehicle; andgenerating the trip data based on the trip as simulated.

Outputting the determined energy consumption by the vehicle for the tripmay comprise presenting the determined energy consumption by the vehiclefor the trip to a user of the vehicle by a user interface positioned atthe vehicle.

Outputting the determined energy consumption by the vehicle for the tripmay comprise presenting the determined energy consumption by the vehiclefor the trip to a manager of the vehicle by a vehicle management deviceexternal to the vehicle.

According to another broad aspect, the present disclosure describes asystem for estimating energy consumption by a vehicle for a trip by thevehicle, based on trip data representative of the trip, the systemcomprising: at least one processor; and at least one non-transitoryprocessor-readable storage medium communicatively coupled to the atleast one processor, the at least one non-transitory processor-readablestorage medium storing processor-executable instructions which, whenexecuted by the at least one processor, cause the system to: identify,by the at least one processor, a distance of travel for the trip basedon a plurality of geographic positions represented in the trip data;identify, by the at least one processor, a duration of the trip based ona plurality of timestamps in the trip data; identify, by the at leastone processor, an ambient temperature of an environment of the vehiclefor the trip based on temperature data; identify, by the at least oneprocessor, speed of the vehicle for the trip based on the trip data;determine, by the at least one processor, energy consumption by thevehicle for the trip as a weighted sum of: energy loss due to vehiclefriction, based on the identified distance of travel; a total time ofthe trip, based on the identified total time of the trip; energy lossdue to temperature control of the vehicle, based on a difference betweenthe identified ambient temperature of the vehicle and an optimaltemperature, for the duration of the trip; and energy loss due to airresistance, based on the identified speed of the vehicle; and output thedetermined energy consumption by the vehicle for the trip.

The processor-executable instructions which cause the at least oneprocessor to identify the distance of travel may cause the at least oneprocessor to: determine respective distances between sequentialgeographic positions represented in the trip data; and sum each of thedetermined respective distances.

The processor-executable instructions which cause the at least oneprocessor to identify a duration of the trip may cause the at least oneprocessor to determine a difference between a first timestampcorresponding to a beginning of the trip and a second timestampcorresponding to an end of the trip.

The temperature data may be separate from the trip data; and theprocessor-executable instructions which cause the at least one processorto identify an ambient temperature of an environment of the vehicle maycause the at least one processor to identify, from the temperature data,ambient temperature during the trip for a geographical regioncorresponding to a geographical region where the trip occurred.

The temperature data may be included in the trip data, and thetemperature data may include a plurality of indications of temperatureof an environment of the vehicle during the trip; theprocessor-executable instructions may further cause the at least oneprocessor to determine an average ambient temperature of an environmentof the vehicle over the trip, by averaging temperature indicated in theplurality of indications of temperature of an environment of the vehicleduring the trip; and energy loss due to temperature control of thevehicle may be based on a difference between the determined averageambient temperature and an optimal temperature, for the duration of thetrip.

The temperature data may be included in the trip data, and thetemperature data may include a plurality of indications of temperatureof an environment of the vehicle during the trip; and energy loss due totemperature control of the vehicle is based on a respective differencebetween each indication of ambient temperature of the vehicle and anoptimal temperature, over the duration of the trip.

The processor-executable instructions which cause the at least oneprocessor to identify a speed of the vehicle may cause the at least oneprocessor to identify speed of the vehicle for a plurality of segmentsof the trip; and energy loss due to air resistance for the trip may bedetermined as a summation of energy loss due to air resistance for eachsegment of the trip, based on the identified speed of the vehicle foreach segment of the trip. The processor-executable instructions whichcause the at least one processor to identify speed of the vehicle for aplurality of segments of the trip may cause the at least one processorto: identify each segment of the trip as being between sequentialgeographic positions indicated in the trip data with correspondingtimestamps; and identify speed of the vehicle for each segment of thetrip by determining, for each segment of the trip, distance of thesegment as distance between sequential geographic positionscorresponding to the respective segment, duration of the segment asdifference in time between timestamps corresponding to a beginning ofthe segment and an end of the segment, and dividing the respectivedistance by the respective duration for the segment.

The weighted sum may further include energy consumed to impart kineticenergy to the vehicle, based on change in speed of the vehicle duringthe trip. The weighted sum may further include energy recovered by aregenerative braking system of the vehicle, based on change in speed ofthe vehicle during the trip.

The system may further comprise a telematic monitoring device positionedat the vehicle, and the processor-executable instructions may furthercause the system to collect, by the telematic monitoring device, thetrip data.

The processor-executable instructions may further cause the at least oneprocessor to: simulate the trip by the vehicle; and generate the tripdata based on the trip as simulated.

The system may further comprise a user interface positioned at thevehicle, and the processor-executable instructions which cause thesystem to output the determined energy consumption by the vehicle forthe trip may cause the user interface to present the determined energyconsumption by the vehicle for the trip to a user of the vehicle.

The system may further comprise a management device external to thevehicle, and the processor-executable instructions which cause thesystem to output the determined energy consumption by the vehicle forthe trip may cause the system to present the determined energyconsumption by the vehicle for the trip to a manager of the vehicle bythe management device.

According to yet another broad aspect, the present disclosure describesa device for estimating energy consumption by a vehicle for a trip bythe vehicle, based on trip data representative of the trip, the devicecomprising: at least one sensor interface for receiving sensor data; atleast one processor; and at least one non-transitory processor-readablestorage medium communicatively coupled to the at least one processor,the at least one non-transitory processor-readable storage mediumstoring processor-executable instructions which, when executed by the atleast one processor, cause the device to: identify, by the at least oneprocessor, a distance of travel for the trip based on a plurality ofgeographic positions represented in the trip data; identify, by the atleast one processor, a duration of the trip based on a plurality oftimestamps in the trip data; identify, by the at least one processor, anambient temperature of an environment of the vehicle for the trip basedon temperature data; identify, by the at least one processor, speed ofthe vehicle for the trip based on the trip data; determine, by the atleast one processor, energy consumption by the vehicle for the trip as aweighted sum of: energy loss due to vehicle friction, based on theidentified distance of travel; a total time of the trip, based on theidentified total time of the trip; energy loss due to temperaturecontrol of the vehicle, based on a difference between the identifiedambient temperature of the vehicle and an optimal temperature, for theduration of the trip; and energy loss due to air resistance, based onthe identified speed of the vehicle; and output the determined energyconsumption by the vehicle for the trip.

The processor-executable instructions which cause the at least oneprocessor to identify the distance of travel may cause the at least oneprocessor to: determine respective distances between sequentialgeographic positions represented in the trip data; and sum each of thedetermined respective distances.

The processor-executable instructions which cause the at least oneprocessor to identify a duration of the trip may cause the at least oneprocessor to determine a difference between a first timestampcorresponding to a beginning of the trip and a second timestampcorresponding to an end of the trip.

The temperature data may be separate from the trip data; and theprocessor-executable instructions which cause the at least one processorto identify an ambient temperature of an environment of the vehicle maycause the at least one processor to identify, from the temperature data,ambient temperature during the trip for a geographical regioncorresponding to a geographical region where the trip occurred.

The temperature data may be included in the trip data, and thetemperature data may include a plurality of indications of temperatureof an environment of the vehicle during the trip; theprocessor-executable instructions may further cause the at least oneprocessor to determine an average ambient temperature of an environmentof the vehicle over the trip, by averaging temperature indicated in theplurality of indications of temperature of an environment of the vehicleduring the trip; and energy loss due to temperature control of thevehicle may be based on a difference between the determined averageambient temperature and an optimal temperature, for the duration of thetrip.

The temperature data may be included in the trip data, and thetemperature data may include a plurality of indications of temperatureof an environment of the vehicle during the trip; and energy loss due totemperature control of the vehicle may be based on a respectivedifference between each indication of ambient temperature of the vehicleand an optimal temperature, over the duration of the trip.

The processor-executable instructions which cause the at least oneprocessor to identify a speed of the vehicle may cause the at least oneprocessor to identify speed of the vehicle for a plurality of segmentsof the trip; and energy loss due to air resistance for the trip may bedetermined as a summation of energy loss due to air resistance for eachsegment of the trip, based on the identified speed of the vehicle foreach segment of the trip. The processor-executable instructions whichcause the at least one processor to identify speed of the vehicle for aplurality of segments of the trip may cause the at least one processorto: identify each segment of the trip as being between sequentialgeographic positions indicated in the trip data with correspondingtimestamps; and identify speed of the vehicle for each segment of thetrip by determining, for each segment of the trip, distance of thesegment as distance between sequential geographic positionscorresponding to the respective segment, duration of the segment asdifference in time between timestamps corresponding to a beginning ofthe segment and an end of the segment, and dividing the respectivedistance by the respective duration for the segment.

The weighted sum may further include energy consumed to impart kineticenergy to the vehicle, based on change in speed of the vehicle duringthe trip. The weighted sum may further include energy recovered by aregenerative braking device of the vehicle, based on change in speed ofthe vehicle during the trip.

The device may comprise a telematic device positioned at the vehicle.The sensor interface may comprise a communication interface to receivethe sensor data from at least one sensor external to the telematicdevice; and the processor-executable instructions may further cause thedevice to collect, by the telematic device, the trip data including thesensor data from the at least one sensor external to the telematicdevice. The sensor interface may comprise at least one sensor to capturethe sensor data; and the processor-executable instructions may furthercause the device to collect, by the telematic monitoring device, thetrip data including the sensor data from the at least one sensor. 14.The sensor data may include first sensor data and second sensor data;the sensor interface may comprise a communication interface to receivethe first sensor data from at least one sensor external to the telematicmonitoring device; the sensor interface may comprise at least one sensorto capture the second sensor data; and the processor-executableinstructions may further cause the device to collect, by the telematicmonitoring device, the trip data including the first sensor data and thesecond sensor data.

The processor-executable instructions may further cause the at least oneprocessor to: simulate the trip by the vehicle; and generate the tripdata based on the trip as simulated.

The device may further comprise a user interface, and theprocessor-executable instructions which cause the device to output thedetermined energy consumption by the vehicle for the trip may cause theuser interface to present the determined energy consumption by thevehicle for the trip to a user of the vehicle.

Outputting the determined energy consumption by the vehicle for the tripmay comprise outputting the determined energy consumption by the vehiclefor the trip to a management device.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary non-limiting embodiments are described with reference to theaccompanying drawings in which:

FIG. 1 is a schematic view of a system for managing data for a pluralityof vehicles.

FIG. 2 is a flowchart diagram which illustrates an exemplary method fordetermining energy loss or consumption for a vehicle trip.

FIG. 3 illustrates a plot which shows energy consumption versus dragresistance factor for a plurality of trips.

FIG. 4 is a flowchart diagram which illustrates an exemplary method forassessing suitability of a vehicle for a trip.

FIGS. 5A, 5B, 5C, and 5D show efficiency curves as a function of speedat a plurality of temperatures, for respective vehicles.

FIGS. 6A and 6B are exemplary plots of speed as a function of time,which illustrate kinetic energy losses of a vehicle.

FIG. 7 is a schematic diagram of an exemplary telematic device whichcouples to a vehicle.

FIG. 8 is a schematic diagram of an exemplary display.

DETAILED DESCRIPTION

The present disclosure details systems, devices, methods, and models foranalyzing range of battery-powered vehicles.

FIG. 1 is a schematic view of a system 100 for managing data for aplurality of vehicles. FIG. 1 shows a management device 110, whichincludes at least one processor 114, at least one non-transitoryprocessor-readable storage medium 116, and a communication interface118. Although illustrated as one device, management device 110 caninclude a plurality of devices, a plurality of processors 114, aplurality of non-transitory processor-readable storage mediums 116,and/or a plurality of communication interfaces 118. Further, such aplurality of management devices can be in close proximity (e.g. in acentral server location), or can be distributed across differentlocations (e.g. as remote devices). Communication interface 118 can be awired or wireless interface, through which management device 110communicates with other devices, such as a plurality of vehicles,vehicle devices, or user devices.

In the illustrated example, management device 110 is shown ascommunicating with vehicle devices in four vehicles 120 a, 120 b, 120 c,and 120 d (collectively referred to as vehicles 120). However,management device 110 could communicate with vehicle devices in anyappropriate number of vehicles, such as one vehicle, dozens of vehicles,hundreds of vehicles, thousands of vehicles, or even more vehicles. Insome exemplary implementations, management device 110 is a telematicsserver, which collects and stores telematics data for a fleet ofvehicles. In other exemplary implementations, management device 110 is alocation-specific device, which manages vehicles for a particularlocation (or vehicles for a plurality of locations). In any of theseexamples, management device 110 can be used to monitor state of chargeof batteries for vehicles, and thus can be used to prioritize chargingof certain vehicles that need it most (e.g. because charge level is low,and/or is insufficient for an upcoming trip). In some implementationssuch prioritization of charging is performed on a per-location basis, toassist drivers and/or jockeys to couple vehicles to charge stations asappropriate to optimize charging of the fleet, as discussed in moredetail later.

Vehicle 120 a includes at least one processor 124 a, at least onenon-transitory processor-readable storage medium 126 a, and acommunication interface 128 a. Together, the at least one processor 124a, the at least one non-transitory processor-readable storage medium 126a, and the communication interface 128 a can be referred to as “vehicledevice” 122 a.

Vehicle 120 b includes at least one processor 124 b, at least onenon-transitory processor-readable storage medium 126 b, and acommunication interface 128 b. Together, the at least one processor 124b, the at least one non-transitory processor-readable storage medium 126b, and the communication interface 128 b can be referred to as “vehicledevice” 122 b.

Vehicle 120 c includes at least one processor 124 c, at least onenon-transitory processor-readable storage medium 126 c, and acommunication interface 128 c. Together, the at least one processor 124c, the at least one non-transitory processor-readable storage medium 126c, and the communication interface 128 c can be referred to as “vehicledevice” 122 c.

Vehicle 120 d includes at least one processor 124 d, at least onenon-transitory processor-readable storage medium 126 d, and acommunication interface 128 d. Together, the at least one processor 124d, the at least one non-transitory processor-readable storage medium 126d, and the communication interface 128 d can be referred to as “vehicledevice” 122 d.

Collectively, vehicle 120 a, vehicle 120 b, vehicle 120 c, and vehicle120 d can be referred to as “vehicles 120”. Collectively, the at leastone processor 124 a, the at least one processor 124 b, the at least oneprocessor 124 c, and the at least one processor 124 d can be referred toas “processors 124”. Collectively, the at least one non-transitoryprocessor-readable storage medium 126 a, the at least one non-transitoryprocessor-readable storage medium 126 b, the at least one non-transitoryprocessor-readable storage medium 126 c, and the at least onenon-transitory processor-readable storage medium 126 d can be referredto as “non-transitory processor-readable storage mediums 126”.Collectively, communication interface 128 a, communication interface 128b, communication interface 128 c, and communication interface 128 d canbe referred to as “communication interfaces 128”. Collectively, vehicledevice 122 a, vehicle device 122 b, vehicle device 122 c, and vehicledevice 122 d can be referred to as “vehicle devices 122”.

Any of the communication interfaces 128 can be a wired interface or awireless interface, or a vehicle device can include both a wiredcommunication interface and a wireless communication interface.

Each of vehicle devices 122 can be a monolithically packaged device(i.e. a device contained in a single housing) which is installed in arespective vehicle. For example, any of vehicle devices 122 could be atelematics device, which plugs into the respective vehicle (e.g. at theOBDII port). Such telematics devices can gather vehicle information fromthe vehicle, from sensors built into the telematics device itself, andcommunicate said information to management devices such as managementdevice 110. However, this is not necessarily the case, and each vehicledevice 122 can refer to the collection of components installed in avehicle (i.e. they do not have to be packaged in a single housing). Asan example, a vehicle manufacturer could install processing, storage,and communication equipment in vehicles for the purpose of collecting,processing, and transmitting data. Further, components of any of thevehicle devices 122 can be multi-purpose components which serve otherfunctions within the vehicle.

FIG. 1 also shows an optional device 130, which includes at least oneprocessor 134, at least one non-transitory processor-readable storagemedium 136, and a communication interface 138. Although illustrated asone device, device 130 can include a plurality of devices, a pluralityof processors 134, a plurality of non-transitory processor-readablestorage mediums 136, and/or a plurality of communication interfaces 138.Further, such a plurality of devices can be in close proximity (e.g. ina central server location), or can be distributed across differentlocations (e.g. as remote devices). Communication interface 138 can be awired or wireless interface, through which device 130 communicates withother devices.

In the illustrated example, device 130 communicates with managementdevice 110 via communication interfaces 118 and 138. Such communicationcan be direct or indirect (e.g. over the internet or any other network).Device 130 can perform processing and provide data to management device110, which management device 110 in turn uses to manage at least onefleet or group of vehicles (e.g. vehicles 120). As an example,management device 110 may be owned by one entity, which manages a fleetof vehicles. Device 130 may belong to another entity, which providesservices to many fleets of vehicles. As a result, device 130 may haveaccess to more vehicle data (i.e. data from a larger quantity ofvehicles) compared to management device 110. In an exemplary use case,device 130 may generate range metrics, models, or profiles as discussedin detail later for at least one plurality of vehicles, based on a largeamount of vehicle data available to device 130. In this exemplary usecase, device 130 communicates such metrics, models, or profiles tomanagement device 110, which management device 110 then uses to performanalysis, assessment, or prediction for similar vehicles in a fleetmanaged by management device 110 (e.g. vehicles 120). In this way,management device 110 can assess models for vehicles based on a largeamount of statistical data that management device 110 itself does nothave access to. As another example, management device 110 may be amanagement device for a specific location (e.g. vehicle lot, warehouse,or hub), such that management device 110 manages vehicles which operateout of said location. In such an example, device 130 may be a fleetmanagement device, which manages vehicles in a fleet across multiplelocations (e.g. all locations, or a subset of locations).

FIG. 2 is a flowchart diagram which illustrates an exemplary method 200for determining energy loss or consumption by a vehicle for a trip bythe vehicle. In some cases, method 200 can be performed after a trip hasbeen travelled by the vehicle, based on trip data for the trip astravelled (e.g. method 200 is performed based on historical trip data).In other cases, method 200 can be performed based on a modeled orsimulated trip (i.e. the trip is not actually travelled by the vehicle,or is not yet travelled, but is modeled or simulated such as by routeplanning or navigation software). Unless context dictates otherwise,discussion of method 200 can pertain to an actual trip as travelled, ora hypothetical (or expected) trip as modeled. Method 200 as illustratedincludes acts 202, 204, 206, 208, and 210. One skilled in the art willappreciate that additional acts could be added, acts could be removed,or acts could be reordered as appropriate for a given application. Withreference to the example illustrated in FIG. 1 , acts can be performedby appropriate components of management device 110, vehicle devices 122,or optional device 130. For example, acts of identification,determination, or summation can be performed by at least one appropriateprocessor. Further, any of the at least one non-transitoryprocessor-readable storage mediums 116, 126, or 136 could haveinstructions stored thereon, which when executed by a respective atleast one processor (processors 114, 124, or 134) cause the respectivemanagement device 110, vehicle device 122, or optional device 130 toperform a given act of method 200. An act being performed by at leastone processor 124 refers to the act being performed by any of processors124 a, 124 b, 124 c, or 124 d. An act being performed by at least onenon-transitory processor-readable storage medium 126 refers to the actbeing performed by any of non-transitory processor-readable storagemediums 126 a, 126 b, 126 c, or 126 d. An act being performed bycommunication interface 128 refers to the act being performed by any ofcommunication interfaces 128 a, 128 b, 128 c, or 128 d. Typically, for acombination of acts performed by a combination of at least oneprocessor, at least one non-transitory processor-readable storagemedium, and a communication interface of a vehicle device, thecombination of acts are performed by at least one processor, at leastone non-transitory processor-readable storage medium, and acommunication interface common to one of vehicle devices 122 a, 122 b,122 c, or 122 d (or any other similar vehicle device). Generallyspeaking, in the context of method 200 acts of identification anddetermination are performed by at least one processor (e.g. any ofprocessors 114, 124, or 134). Thus, reference to an act of identifyingor determining being performed by a particular device generally refersto the act being performed by at least one processor of the device.

For method 200, trip data for a trip of a vehicle is received. Forexample, during a trip, any of vehicle devices 122 can capture, sense,or record data for the trip performed by a corresponding vehicle 120.Such captured trip data could include, as non-limiting examples: aplurality of geographic positions of the vehicle during the trip; aplurality of timestamps (e.g. a timestamp associated with eachgeographic position of the vehicle); temperature data of an environmentof the vehicle; speed or velocity data of the vehicle; or any otherappropriate data. As another example, for an expected trip (i.e. a tripwhich has not yet occurred), expected data for the trip can be modeled.Such modeled data could include, as non-limiting examples: a pluralityof expected geographic positions of the vehicle during the trip; aplurality of expected timestamps (e.g. a timestamp associated with eachgeographic position of the vehicle, or expected timestamps at thebeginning or end of the trip); temperature data representing expectedtemperature of an environment of the vehicle; expected speed or velocitydata of the vehicle; or any other appropriate data.

In examples where acts 202, 204, 206, 208, and/or 210 are performed by adevice other than the vehicle device 122 (such as management device 110or optional device 130), receiving the trip data can comprise receivingtrip data as captured by a vehicle device (similar to as above), over acommunication interface (such as communication interfaces 118 and/or138).

In examples where the trip data is for a simulated trip (as opposed totrip data collected during an actual trip), receiving the trip data cancomprise receiving (by at least one processor of any of managementdevice 110, vehicle devices 122, optional device 130, or any otherappropriate device) the trip data as generated for the simulated trip.That is, at least one processor of any of management device 110,vehicles device 122, optional device 130, or any other appropriatedevice can generate simulated trip data as discussed later. Thissimulated trip data can be stored by at least one non-transitoryprocessor-readable storage medium (e.g. non-transitoryprocessor-readable storage mediums 116, 126, or 136) and accessed by atleast one processor performing method 200 as necessary. Alternatively,the simulated trip data can be generated live and used immediately aftergeneration in the context of method 200.

At 202, a distance of travel for the trip is identified based on aplurality of geographic positions represented in the trip data. As oneexample, a distance could be determined between a first geographicposition of the vehicle at a beginning of the trip and a secondgeographic position of the vehicle at an end of the trip. Such adetermined distance is a linear approximation of the trip. However, manytrips are non-linear, and as such more accurate distance determiningmethods could be utilized. In one exemplary implementation, respectivedistances between sequential geographic positions represented in thetrip data can be determined, then each of the determined respectivedistances can be summed. That is, for a plurality of geographicpositions represented in the trip data, a respective distance can bedetermined between a given geographic position and a sequentialgeographic position; this can be performed for each geographic positionrepresented in the trip data, such that a plurality of distances aredetermined which correspond to a piece-wise representation of the trip.These distances can be summed, resulting in a total distance for thetrip. In this context, “sequential” geographic positions can bedetermined based on timestamps associated with each geographic position.Alternatively, “sequential” geographic positions can be determined basedon proximity between geographic positions (e.g. a second geographicposition can be considered “sequential” to a first geographic positionif the second geographic position is spatially the closest geographicposition to the first geographic position). Alternatively, “sequential”geographic positions can be determined based on a specified sequence,such as a sequence included in the trip data (e.g., geographic positionscan be labelled with sequential index metadata, which specifies asequence of the geographic positions). Further, though this discussionrefers to determining distance between each geographic position in theplurality of geographic positions, it is not necessary that theplurality of geographic positions includes every single piece ofgeographic position data that may have been captured during the trip ormodeled for the trip. In some implementations, some geographic positiondata may be filtered out from the trip data. For example, suspectederroneous data could be removed (e.g. data which strays unreasonably farfrom other geographic position data). As another example, redundantgeographic position data could be removed to reduce data size (e.g.,geographic position data which does not meaningfully further refinerepresentation of the trip, such as intermediate geographic positionsalong a straight line travelled by the vehicle during the trip). Asanother example, the trip data may be “compressed” by dropping some data(e.g., even though many geographic positions may be captured or modeled,geographic position may only be reported or included in the trip data atcertain intervals).

At 204, a duration of the trip is identified based on a plurality oftimestamps in the trip data. For example, a difference in time between afirst timestamp and a second timestamp can be determined, where thefirst timestamp corresponds to a beginning of the trip, and the secondtimestamp corresponds to an end of the trip. Such a difference in timerepresents a duration of the trip.

At 206, an ambient temperature of an environment of the vehicle isidentified based on temperature data. This could be performed in severaldifferent ways.

In a first implementation, the temperature data is separate from thetrip data, and identifying an ambient temperature of an environment ofthe vehicle comprises identifying, from the temperature data, ambienttemperature during the trip for a geographical region corresponding to ageographical region where the trip occurred. For example, thetemperature data may be received from a weather and temperaturemonitoring service or entity (e.g. a weather service), and temperaturedata for the geographical region corresponding to a region where thetrip occurred or is modeled as occurring can be identified bycross-referencing geographic position data for the vehicle for the tripwith regional temperature information (or temperature predictioninformation) in the temperature data. For a trip where the vehicletraverses a plurality of regions for which different regionaltemperature information is available in the temperature data, anappropriate region can be selected in multiple ways discussed below. Inone case, temperature data for any one region traversed (or modeled tobe traversed) by the vehicle (e.g. a region where the trip begins, aregion where the trip ends, or a region therebetween) could beidentified as the ambient temperature of an environment of the vehiclefor the trip. For greater accuracy, for each region through which thevehicle traverses or is modeled to traverse during the trip, temperaturedata for the respective region can be identified as the ambienttemperature of an environment of the vehicle for a respective segment ofthe trip. Even if a vehicle is within a particular region during a trip,temperature within the region may change over time during the trip. Fora trip where different regional temperature information or predicationsare available in the temperature data for different times during thetrip, an appropriate time period can be selected in multiple waysdiscussed below. In one case, temperature data for any one time periodduring the trip (e.g. a beginning of the trip, an end of the trip, or amidpoint of the trip) could be identified as the ambient temperature ofan environment of the vehicle during the trip. For greater accuracy, foreach time period having different temperature experienced (or expectedto be experienced) by the vehicle during the trip, temperature data forthe respective time period can be identified as the ambient temperatureof an environment of the vehicle for the respective time period of thetrip. In determining energy consumption for the trip, temperature fordifferent segments or time periods of the trip can be accounted for in apiece-wise or an integrated manner as is discussed later with referenceto Formulas (3) and (4).

In a second implementation, the temperature data is included in the tripdata, and the temperature data includes a plurality of indications oftemperature of an environment of the vehicle during the trip. Forexample, the vehicle can be equipped with a temperature sensor whichcollects temperature data of an environment of the vehicle. Thistemperature data can be included with other trip data captured by thevehicle. In such an example, the collected temperature data can includea plurality of indications of temperature of an environment of thevehicle during the trip. In some cases, the collected temperature datacould be used as-is, as discussed with reference to Formula (4) below.In other cases, from such temperature data, ambient temperature of thevehicle during the trip can be simplified or generalized to reduceprocessing burden in determining energy consumption for temperaturecontrol of the vehicle. In one example, an average ambient temperatureof an environment of the vehicle over the trip can be determined, byaveraging temperature indicated in the plurality of indications oftemperature of an environment of the vehicle during the trip. In anotherexample, a plurality of average ambient temperatures of an environmentof the vehicle can be determined, each average temperature in theplurality of ambient temperatures representing a sub-portion of thetrip, by averaging temperature indicated in the plurality of indicationsof temperature of an environment of the vehicle for the respectivesub-portion of the trip.

At 208, speed of the vehicle is identified based on the trip data. Thiscan be performed in several ways discussed below.

In a first implementation, speed of the vehicle is identified for aplurality of segments of the trip. For example, for trip data whichincludes a plurality of geographic positions paired with correspondingtimestamps, each segment of the trip can be considered as being betweensequential geographic positions. In such a case, distance of the segmentis identified as distance between sequential geographic positionscorresponding to the segment, and duration of the segment is identifiedas difference in time between timestamps corresponding to a beginning ofthe segment and an end of the segment (difference in time betweentimestamps corresponding to sequential geographic positions). For eachsegment, distance of the respective segment is divided by respectiveduration for the segment (that is, distance of the segment is divided bythe time it takes the vehicle to travel the segment). In this way, speedof the vehicle for each segment is determined.

In a second implementation, the trip data includes speed data. Forexample, the vehicle can include a velocity or speed sensor, and speeddata or velocity data collected from the velocity or speed sensor can beincluded in the trip data. As another example, speed data could compriseexpected speed during the trip, such as based on speed limits, or basedon typical speeds along portions of the trip. Such speed data could bepaired with segments of the trip (such as segments delineated bygeographic position as discussed above), or could be consideredindependently.

At 210, energy consumption by the vehicle for the trip is determined asa weighted sum, based on the distance identified in act 202, theduration identified in act 204, the ambient temperature identified inact 206, and the speed identified in act 208. In particular, act 210involves determining a weighted sum of: energy loss due to vehiclefriction, based on the identified distance of travel; energy loss due toauxiliary vehicle functions, based on the identified total time of thetrip; energy loss due to temperature control of the vehicle, based on adifference between the identified ambient temperature of the vehicle andan optimal temperature, for the duration of the trip; and energy lossdue to air resistance, based on the identified speed of the vehicle. Aperson of ordinary skill in the art could modify the elements includedin the weighted sum as appropriate, including removing elements from thesum, adding elements to the sum, or altering the basis of elements ofthe sum. As one example, kinetic energy loss due to braking couldoptionally be included in the sum, as discussed later with reference toFormula (12) and FIGS. 6A and 6B.

The weighted sum in act 210 can be expressed by Formula (1) below:

$\begin{matrix}{E_{tot} = \alpha_{1}D + \alpha_{2}T + \alpha_{3}\Delta T + \alpha_{4}R} & \text{­­­(1)}\end{matrix}$

E_(tot) represents total energy consumed or used (energy loss) from thevehicle battery over the course of the trip. Each of the terms ofFormula (1) are discussed in detail below.

The term “α₁D” represents energy loss due to vehicle friction, where α₁is a first weight coefficient, and D is distance travelled over thetrip. Vehicle friction in this case refers to friction sources which arerelatively unaffected by operator-controllable factors (factors such asvelocity or acceleration). Examples include motor friction, axlefriction, road friction (friction between tires and road), etc. Vehiclefriction is also affected by mass of the vehicle, but in this examplemass does not need to be explicitly expressed in Formula (1). Instead,mass is represented within the first weight coefficient α₁, since massis generally specific to a vehicle being modelled by Formula (1) (withsome variation for cargo).

The term “α₂T” represents energy loss due to auxiliary vehiclefunctions, where α₂ is a second weight coefficient, and Tis duration(time) of the trip. In this model, the rate of energy loss due toauxiliary vehicle functions (e.g. infotainment, lights, etc.) is assumedto be constant over the course of the trip (hence the calculation beinga weighted factor multiplied by the duration of the trip. In someimplementations however, the term “α₂T” could be multiplied by anotherfactor (or coefficient α₂ could be modified) to account for variableauxiliary energy consumption. For example, if the trip takes place atnight (or if the trip data includes data indicating that the vehiclelights were on during the trip), the term “α₂T′ could be multiplied by a“lights” coefficient, or coefficient α₂ could be increased to representenergy consumption by the vehicle lights, so as to more accuratelyreflect auxiliary energy loss during the trip.

The term “α₃ΔT” represents energy loss due to temperature control of thevehicle, where α₃ is a third weight coefficient, Δ represents adifference between identified ambient temperature of an environment ofthe vehicle and an optimal temperature, and Tis duration (time) of thetrip. Δ can be represented mathematically as in Formula (2) below:

$\begin{matrix}{\Delta = \left| {t_{amb} - t_{opt}} \right|} & \text{­­­(2)}\end{matrix}$

t_(amb) represents the identified ambient temperature of an environmentof the vehicle, and t_(opt) represents an optimal temperature of thevehicle. The optimal temperature t_(opt) generally corresponds to atemperature which is suitable for human occupation, e.g. 20° C., thoughoptimal temperature could be set as appropriate for a given application.Generally, the greater the difference between ambient temperature of anenvironment of the vehicle and optimal temperature, the more an operatorof the vehicle will use climate control functions of the vehicle (e.g.cabin heating or air conditioning), which results in more energy lossfor the trip.

For implementations where ambient temperature of an environment of thevehicle is determined as a single value (e.g. cases where a singleambient temperature for a region is identified, or cases where anaverage ambient temperature for the trip is determined as discussedabove), the term “α₃ΔT” can be performed as a multiplication as shown inFormula (1).

For implementations where ambient temperature of an environment of thevehicle is determined as a plurality of values (e.g. cases where aplurality of ambient temperatures are identified corresponding tomultiple regions of the trip, cases where a plurality of ambienttemperatures are identified corresponding to temperatures experienced by(or modeled as being experienced by) the vehicle during different timeperiods of the trip, or cases where a temperature sensor of the vehiclecaptures temperature data representing a plurality of temperaturesexperienced by the vehicle during the trip), the term “α₃ΔT” can beexpanded to account for changing ambient temperature. For example, thisterm could be expressed according to Formula (3) below:

$\begin{matrix}{\alpha_{3}\Delta T = \alpha_{3}{\sum_{i = 1}^{n}\Delta_{i}} \ast T_{i} = \alpha_{3}{\sum_{i = 1}^{n}{\left| {t_{amb.i} - t_{opt}} \right| \ast T_{i}}}} & \text{­­­(3)}\end{matrix}$

In Formula (3), for a total number of different temperature segments n(periods of time or regions where ambient temperature different fromother periods of time during the trip), respective Δ for each segment(Δ_(i) or | t_(amb.i) ―t_(opt) |) is multiplied by respective durationfor each corresponding segment (T_(i)). The result is summed, andmultiplied by the third weight coefficient α₃. In this way, energy lossdue to climate control is determined in a piece-wise manner (that is,energy loss per temperature segment is calculated on a per segment basisand summed). In some cases, with enough temperature data, Δ could bemodeled as a temperature curve (profile, equation, etc.) over time(temperature as a function of time: Δ(t)). In such cases, integrationcan be used to evaluate the term “α₃ΔT” as per Formula (4) below:

$\begin{matrix}{\alpha_{3}\Delta T = \alpha_{3}{\int_{0}^{T}\Delta}(t)dt} & \text{­­­(4)}\end{matrix}$

The term “α₄R” in Formula (1) represents energy loss due to airresistance on the vehicle over the trip, where α₄ is a fourth weightcoefficient, and R is a drag factor representing air resistance. Powerto overcome drag due to air resistance is proportional to speed of thevehicle cubed; based on this, energy loss due to air resistance (theterm “α₄R”) over the trip is represented by Formula (5) below:

$\begin{matrix}{\alpha_{4}R = \alpha_{4}{\int_{0}^{T}{v(t)^{3}}}dt} & \text{­­­(5)}\end{matrix}$

In Formula (5), v(t) represents a speed curve (profile, equation, etc.)over time (speed as a function of time). If enough speed data isavailable, such that vehicle speed over the trip is modelled by afunction v(t), Formula (5) can be used directly to determine the term“α₄R” in Formula (1). In some cases, however, speed data may only beavailable for the trip in segments. In such cases, the term “α₄R” inFormula (1) can be determined in a piece-wise manner in accordance withFormula (6) below:

$\begin{matrix}{\alpha_{4}R = \alpha_{4}{\sum_{k = 1}^{q}{v_{k}{}^{3}}} \ast T_{k} = \alpha_{4}{\sum_{k = 1}^{q}{v\left( t_{k} \right)^{3}}} \ast \left( {t_{k} - t_{k - 1}} \right)} & \text{­­­(6)}\end{matrix}$

For a total number of trip segments q, a speed of the vehicle v_(k) foreach segment k is multiplied by a duration of the segment T_(k). Inaccordance with a specific implementation, the rightmost representationin Formula (6) shows, for a total number of trip segments q, a speed ofthe vehicle v at timestamp t_(k) (a timestamp at an end of segment k)for each segment is multiplied by a duration of the segment t_(k) -t_(k-1) (where t_(k) represents a timestamp at an end of the segment k,and t_(k-1) represents a timestamp at a beginning of the segment k).

The trip segments in Formula (6) (represented by k and total number q),and the temperature segments in Formula (3) (represented by i and totalnumber n) are not necessarily the same, hence why different labels areused. A trip can be segmented in a different number of temperaturesegments based on temperature data, temperature change times andregions, and/or data collection times of a temperature sensor.Similarly, a trip can be segmented in a different number of tripsegments based on geographic position data, speed data, and/or datacollection times of geographic position sensor or speed sensor.

The weight coefficients α₁, α₂, α₃, and α₄ can be determined by leastsquares regression, based on energy consumption data from a plurality oftrips. This is shown with reference to FIG. 3 discussed below.

FIG. 3 illustrates a plot 300, which shows total energy consumptionE_(tot) versus drag resistance factor R discussed above with referenceto Formulas (5) and (6). FIG. 3 illustrates trips by a specific vehicletype (make and model), but is not necessarily limited to trips by asingle vehicle. In essence, FIG. 3 shows energy consumption by thevehicle proportional to speed (a factor which an operator hassignificant control over). By performing least squares regression withreference to Formula (1), and filling known data (mass, distance, time,temperature, speed), weighting coefficients α₁, α₂, α₃, and α₄ can bedetermined for the vehicle type. This can be performed similarly fordifferent vehicle types, to arrive at respective representations ofenergy consumption for the different vehicle types.

Based on the determined α₁, α₂, α₃, and α₄, energy consumption for atrip or trips can be determined based on Formula (1). A process for thisis discussed below with reference to FIG. 4 .

FIG. 4 is a flowchart diagram which illustrates an exemplary method 400.Method 400 as illustrated includes acts 402, 404, and 406. One skilledin the art will appreciate that additional acts could be added, actscould be removed, or acts could be reordered as appropriate for a givenapplication. With reference to the example illustrated in FIG. 1 , actscan be performed by appropriate components of management device 110 orvehicle devices 122. For example, acts of identification, determination,summation, estimation, or comparison can be performed by at least oneappropriate processor. Further, any of the at least one non-transitoryprocessor-readable storage mediums 116 or 126 could have instructionsstored thereon, which when executed by a respective at least oneprocessor (processors 114 or 124) cause the respective management device110 or vehicle device 122 to perform a given act of method 400. An actbeing performed by at least one processor 124 refers to the act beingperformed by any of processors 124 a, 124 b, 124 c, or 124 d. An actbeing performed by at least one non-transitory processor-readablestorage medium 126 refers to the act being performed by any ofnon-transitory processor-readable storage mediums 126 a, 126 b, 126 c,or 126 d. An act being performed by communication interface 128 refersto the act being performed by any of communication interfaces 128 a, 128b, 128 c, or 128 d. Typically, for a combination of acts performed by acombination of at least one processor, at least one non-transitoryprocessor-readable storage medium, and a communication interface of avehicle device, the combination of acts are performed by at least oneprocessor, at least one non-transitory processor-readable storagemedium, and a communication interface common to one of vehicle devices122 a, 122 b, 122 c, or 122 d (or any other similar vehicle device).

At 402, an indication of at least one trip to be performed by a vehicleis received. In a first example, an operator of a vehicle can input adesired destination or route into a route planning system. Otherexamples are discussed later. The “at least one trip” in act 402includes a trip to this destination or by this route. In this example, anavigation system of a vehicle having at least one processor isdiscussed, but in practice any other appropriate device could beutilized, such as a smartphone. In response to an operator inputting adestination, the at least one processor can determine a route to thedestination (or the operator can input a route directly).

At 404, based on the route, the at least one processor can evaluateexpected energy consumption for a trip to the destination of the route.This can be achieved using Formula (1). The at least one processor canreceive or retrieve pertinent data, such as from a non-transitoryprocessor-readable storage medium at the vehicle (e.g. any ofnon-transitory processor-readable storage mediums 126), or from a remotedevice such as a server (e.g. management device 110 or device 130). Forexample, weight coefficients α₁, α₂, α₃, and α₄ can be retrieved from adatabase of information for the specific vehicle being operated (forexample stored on at least one non-transitory processor-readable storagemedium 126 of a vehicle 120; or stored on at least one non-transitoryprocessor-readable storage medium 116 or 136 remote from the vehicle).Further, the at least one processor can determine expected distance ofthe trip based on the route. Further, the at least one processor candetermine expected duration of the trip, and expected speed profileduring the trip, based on the determined distance and based on speedlimits and/or typical travel speeds along roadways in the route (e.g.from a database which stores roadway information, for example stored onat least one non-transitory processor-readable storage medium 126 of avehicle 120; or stored on at least one non-transitory processor-readablestorage medium 116 or 136 remote from the vehicle). Further, expectedambient temperature during the trip can be identifying based ontemperature data from a temperature provider or database, or based ontemperature data collected by a temperature sensor of the vehicle. Basedon all of this information, the at least one processor can evaluateFormula (1), to estimate expected energy consumption by the vehicle forthe trip.

At 406, the at least one processor compares the estimated energyconsumption by the vehicle to an amount of energy available to thevehicle (e.g. charge level or energy capacity of the vehicle battery).Based on the comparison, the at least one processor can determinewhether completion of the trip is possible or not. Alternatively, the atleast one processor can return a confidence score which indicates howlikely it is the vehicle will be able to complete the trip. The at leastone processor can also estimate expected energy remaining for thevehicle after the trip (e.g. charge level of the battery after thetrip), to provide an operator with the ability to assess travel optionsafter arriving at the destination (e.g. the option to drive to a chargestation, or make a return journey).

The method 400 is not limited to being performed for a vehicle which isavailable (e.g. owned, rented, leased, loaned, or otherwise provided) toan operator. Further, method 400 is also not limited to being performedfor a single trip. An exemplary further implementation in this regard isdiscussed below.

In a second example, a vehicle operator may consider replacing anexisting combustion engine vehicle with a battery-powered vehicle. Priorto making this decision, however, it is beneficial for the operator tounderstand the range capabilities of candidate battery-powered vehicles,and particularly whether candidate battery-powered vehicles can suit theneeds of the operator. An appropriate device, such as a personalcomputer or smartphone, could be used to input routes or trips which theoperator wishes to be able to travel, in accordance with act 402 ofmethod 400. This could be performed manually by the operator, or couldbe automated. In some cases, trip data for previous routes may beaccessible. For example, the operator’s present vehicle can be equippedwith telematics capabilities (e.g. a telematics monitoring device),which can collect trip data regarding where the vehicle has been drivento and from, speeds of travel, temperature data, braking events, or anyother appropriate information. As another example, a navigation systemof the operator’s present vehicle may have travel history stored, whichshows where the vehicle has been driven to. In either case, data whichis not available can be determined or retrieved by at least oneprocessor. For example, if speed data is not available, vehicle speedcan be determined based on geographic position data and timestamps asdiscussed above. As another example, if temperature data is notavailable from the trip data, historical temperature data could beretrieved from a temperature data provider.

Based on data for the desired or historical routes and trips, the atleast one processor can evaluate Formula (1) to estimate energyconsumption for any pertinent number of trips, in accordance with act404, for at least one candidate battery-powered vehicle underconsideration. In accordance with act 406, the at least one processorcan compare the estimated energy consumption to an amount of energyavailable to the vehicle. The compared energy consumption can be for anynumber of trips sequentially (i.e., any number of trips on a singlebattery charge) as appropriate for a given application. If the operatoris satisfied with the capabilities of a particular candidate vehicle fortheir needs, they can proceed to acquire said vehicle.

Additionally, it may be desirable to determine not only whether acandidate vehicle can travel to a desired or historical destination oralong a desired or historical route, but also if the candidate vehiclecan complete a desired plurality of trips within a specified chargingschedule. For example, an operator may wish to know not only whether acandidate vehicle can make it to a desired destination, but also if thecandidate vehicle can make it back from desired destination, on a singlecharge.

In a third example, a fleet manager may be evaluating whether to replacea fleet of existing combustion engine vehicles with battery-poweredvehicles. To minimize disruption to existing business operations, thefleet manager may be interested in determining if a candidate vehicle(or set of candidate vehicles) will be able to complete a plurality oftrips within an existing or non-disruptive energy-replenishmentframework. For example, a fleet manager for a delivery company may onlybe interested in converting to a battery-powered fleet of vehicles ifthe vehicles can make all expected deliveries in a day on a singlecharge, with vehicles being returned to a vehicle depot for chargingovernight. Such a model would avoid business disruption caused by havingto charge battery-powered vehicles mid-day, which can be time-consumingcompared to filling a combustion-based engine with fuel.

In this third example, act 402 comprises receiving an indication of atleast one trip, performed by a plurality of vehicles. Such indicationsof trips could be manually provided, or could be retrieved fromhistorical telematics or navigation data, as described above. Trips maybe grouped on a per-vehicle basis, in order to determine whether atleast one trip corresponding to each vehicle can be performed on asingle charge of a battery of a candidate vehicle. That is, for eachexisting vehicle in the fleet, expected trips to be performed betweenopportunities to charge are identified or input. This could compriseeach trip a vehicle is expected to perform in a day, where the vehiclewill be charged overnight. Such identification of trips could be basedon the actual trips each individual existing vehicle performs, in orderto identify individually whether it would be feasible to replace eachindividual existing vehicle with a candidate battery-power vehicle.Alternatively, identification of trips could be averaged or approximatedto encompass a plurality of vehicles. For example, for a fleet ofvehicles where each vehicle in the fleet is expected to performsimilarly (i.e. have similar range capabilities), the identified tripscould be based on trip data for the plurality of vehicles as a group.Notably, “opportunities to charge” do not necessarily equate to existingtimes when an existing vehicle refuels. For example, while an existingcombustion-engine vehicle may stop for replenishment of combustiblefuel, this may not be feasible for recharging a battery of abattery-powered vehicle. As such, opportunities for charging may differfrom opportunities for replenishing combustible fuel.

In this third example, act 404 comprises estimating energy consumptionby each candidate vehicle, for at least one trip to be performed by eachvehicle on a single charge. This can be performed on a per-vehicle basisas discussed above. In some implementations, processing burden can bereduced by first identifying trips or groups of trips between chargingopportunities (e.g. in act 402) which are likely to be the most energyconsuming. For example, trips or groups of trips of long distance, oflong duration, of extreme temperature, or high speed, etc. can beidentified.

In this third example, act 406 comprises comparing estimated energyconsumption for a trip or group of trips between charging opportunitiesto an amount of energy available to a candidate vehicle. That is, basedon the energy consumption determined for a trip of group of trips to beperformed between charging opportunities, the at least one processordetermines whether a candidate vehicle has sufficient energy availableto perform the trip or group of trips. The most energy consuming groupof trips can be selected for comparison for evaluating feasibility ofreplacing the existing plurality of vehicles, on the grounds that if acandidate vehicle can handle the most energy consuming groups of tripshistorically, then said candidate vehicle should be able to handleday-to-day operations required of vehicles in the fleet. If a fleetmanager has higher risk tolerance, average trip length or trip energyconsumption could be identified for comparison with a candidatebattery-powered vehicle. In such a scenario, if a candidate vehicle canhandle an average day of trips (or an average day plus a safety orexcess margin), then the candidate vehicle should generally be able tohandle day-to-day operation; though in some cases vehicles may need tobe charged midway through a day if energy consumption is particularlyhigh.

Method 400 is not limited to being performed for a single candidatevehicle. In some implementations, a plurality of candidate vehicles canbe evaluated, to identify which candidate vehicles of the plurality ofcandidate vehicles would be capable of completing the identified tripsor groups of trips.

In addition or alternative to determining energy consumption for a trip,Formula (1) can be modified to determine instantaneous efficiency for avehicle. In particular, duration (time) T of a trip can be expressed asin Formula (7) below:

$\begin{matrix}{T = \frac{D}{v}} & \text{­­­(7)}\end{matrix}$

Further, instantaneous resistance R can be expressed as in Formula (8)below:

$\begin{matrix}{R = v^{3}T = v^{3}\frac{D}{v} = v^{2}D} & \text{­­­(8)}\end{matrix}$

Efficiency of a vehicle over a trip can be expressed as energy consumedover the trip, divided by the distance of the trip. This is expressed inFormula (9) below:

$\begin{matrix}{Eff = \frac{E_{tot}}{D} = \alpha_{1} + \left( {\alpha_{2} + \alpha_{3}\Delta} \right)\frac{T}{D} + \,\mspace{6mu}\alpha_{4}\frac{R}{D}} & \text{­­­(9)}\end{matrix}$

Combining Formula (9) with Formulas (7) and (8) results in Formula (10)below:

$\begin{matrix}{Eff = \alpha_{1} + \left( {\alpha_{2} + \alpha_{3}\Delta} \right)\frac{1}{v} + \,\mspace{6mu}\alpha_{4}v^{2}} & \text{­­­(10)}\end{matrix}$

Formula (10) provides a model for efficiency of a vehicle based on speedv and temperature (or specifically difference between ambienttemperature of an environment of the vehicle and an optimal temperature,as discussed above with reference to Formula (2)). Formula (1)represents efficiency as energy consumed per distance travelled (e.g.kWh per km travelled), per Formula (9). Formula (10) can be inverted torepresent efficiency as distance which can be travelled per unit ofenergy (e.g. km which can be travelled per kWh). This is shown inFormula (11) below:

$\begin{matrix}{Eff^{- 1} = \frac{1}{\alpha_{1} + \left( {\alpha_{2} + \alpha_{3}\Delta} \right)v^{- 1} + \alpha_{4}v^{2}}} & \text{­­­(11)}\end{matrix}$

FIGS. 5A, 5B, 5C, and 5D illustrate several exemplary plots which showefficiency (as Eff⁻¹) versus speed, at different temperatures, fordifferent vehicles. Specifically, each of FIGS. 5A, 5B, 5C, and 5Dillustrate a respective efficiency versus speed curve for a respectivevehicle at 20° C., 10° C., 0° C., -10° C., and -20° C. In each of FIGS.5A, 5B, 5C, and 5D, speed is represented in kph (kilometers per hour),efficiency (as Eff⁻¹) is represented in km/kWh (kilometers perkilowatt-hour), and temperature is represented in degrees Celsius. FIGS.5A, 5B, and 5C illustrate efficiency curves for different smallpassenger vehicles, whereas FIG. 5D illustrates efficiency curves for alarge cargo van. Based on efficiency curves such as the examplesillustrated in FIGS. 5A, 5B, 5C, and 5D, an optimum vehicle for a givensituation, operator, or fleet can be evaluated, based on expected speedsand temperatures ranges in which the vehicle will be operated. This isuseful for assessing viability of acquiring battery-powered vehicles, asan example.

As mentioned above, additional optional terms can be included in Formula(1) (the weighted sum in act 210) for increased accuracy and/or toaccount for additional factors. One example is expressed in Formula (12)below:

$\begin{matrix}{E_{tot} = \alpha_{1}D + \alpha_{2}T + \alpha_{3}\Delta T + \,\mspace{6mu}\alpha_{4}R + \alpha_{5}K} & \text{­­­(12)}\end{matrix}$

E_(tot) represents total energy consumed or used (energy loss) from thevehicle battery over the course of a trip, similar to as discussedregarding Formula (1) above. Further, the terms “α₁D”, “α₂T”, “α₃ΔT”,and “α₄R” are as discussed above regarding Formula (1) above.

The term “α₅K” in Formula (12) represents kinetic energy loss of thevehicle during the trip, where α₅ is a fourth weight coefficient, and Kis a kinetic energy factor representing kinetic energy losses, and isdiscussed in detail regarding FIGS. 6A and 6B below. Instantaneouskinetic energy E_(k) (how much kinetic energy the vehicle has at a givenmoment) is represented by Formula (13) below:

$\begin{matrix}{E_{k} = \frac{mv^{2}}{2}} & \text{­­­(13)}\end{matrix}$

In Formula (13), m represents mass of the vehicle, and v representsspeed of the vehicle. When vehicle brakes are applied (or speed of thevehicle is reduced for any other reason, such as climbing a hill orreleasing the accelerator pedal), kinetic energy E_(k) of the vehicle isreduced. To increase the speed of the vehicle, energy is drawn from thebattery to power the vehicle motor. Absent a regenerative brakingsystem, the kinetic energy reduction during speed reduction is lostenergy for the trip. With a regenerative braking system, however, aportion of the kinetic energy can be recovered and converted into storedenergy in the vehicle battery. Depending on specific vehicle, brakingsystem, severity of braking (how hard the brakes are applied), etc.,regenerative braking can recover generally between 30% to 70% of kineticenergy reduced during braking.

FIGS. 6A and 6B are exemplary plots which show speed (v) as a functionof time (t), and illustrate the effect of speed change over a trip onkinetic energy losses. For trip 600 illustrated in FIG. 6A, speed startsat v₀, increases to v_(max), and decreases back to v₀. Such a tripinvolves minimal energy loss or consumption due to change in kineticenergy, in that the vehicle only once accelerates up to v_(max), and asignificant portion of energy can be recovered by regenerative brakingwhen slowing back down to v₀. For trip 602 illustrated in FIG. 6B on theother hand, even though the minimum speed for the trip is v₀, and themaximum speed for the trip is v_(max) (the same as trip 600), thevehicle first speeds up to v₁, slows down to v₂, speeds up again tov_(max), slows down again to v₃, speeds up again to v₄, and finallyslows down to v₀. Each time the vehicle speeds up, energy is spent.However, not all of the energy is recovered by regenerative braking whenthe vehicle slows down. As such, the more the speed of a vehicleincreases and decreases during a trip, the more energy is lost(consumed) during the trip. Kinetic energy losses K as in Formula (12)above can be represented as in Formula (14) below:

$\begin{matrix}{K = E_{a} - E_{b}} & \text{­­­(14)}\end{matrix}$

In Formula (14), E_(a) represents energy consumed to accelerate, whereasE_(b) represents energy recovered by regenerative braking. For a vehiclewith no regenerative braking, E_(b) = 0, such that Kinetic Energyconsumed for the trip is equivalent to E_(a) (the energy consumed toaccelerate). For vehicles with regenerative braking systems, energyrecovered by regenerative braking E_(b) can generally be modelled as afraction of energy consumed to accelerate E_(a), as in Formula (15)below:

$\begin{matrix}{E_{b} = f \ast E_{a}} & \text{­­­(15)}\end{matrix}$

In Formula (15), ƒ represents a proportion of energy which is generallyrecoverable by a regenerative braking system (the effectiveness of theregenerative braking system in the context of the vehicle). ModifyingFormula (14) based on Formula (15) results in Formula (16) below:

$\begin{matrix}{K = E_{a} - \left( {f \ast E_{a}} \right) = \left( {1 - f} \right) \ast E_{a}} & \text{­­­(16)}\end{matrix}$

In Formula (12) above, factors that are relatively constant over a tripfor the vehicle being modeled can be expressed as a part of therespective weight coefficient. In the case of the term “α₅K”, the factor(1-f) can be absorbed into the weight coefficient α₅. With this, K canbe reduced to just being dependent on E_(a). Based on Formula (13)above, the energy consumed to accelerate to a speed v is proportional toacceleration a leading up to the speed v. Specifically, by integratingthe rate of change of kinetic energy E_(k), increases and decreases inKinetic Energy can be summed together to result in total Kinetic Energychange over the trip (which is 0 for a trip where the vehicle starts atrest and ends at rest). To determine energy extracted from the batteryto accelerate the vehicle (E_(a)), we can integrate only positiveportions the rate of change of kinetic energy E_(k), as expressed inFormula (17) below:

$\begin{matrix}{E_{a} = {\int_{0}^{T}{\left( \frac{dE_{k}}{dt} \right)^{+}dt =}}{\int_{0}^{T}{ma^{+}(t)dt}}} & \text{­­­(17)}\end{matrix}$

As mentioned above, factors which are relatively constant over a tripfor a vehicle being modeled can be expressed as a part of a respectiveweight coefficient. In the case of Formula (17), mass m can be absorbedinto weight coefficient α₅. Combining Formulas (16) and (17), andabsorbing relatively constant factors into weight coefficient α₅ resultsin Formula (18) below:

$\begin{matrix}{K = {\int_{0}^{T}{a^{+}(t)dt}}} & \text{­­­(18)}\end{matrix}$

To summarize, the term α₅K in Formula (12) represents weight coefficientα₅ multiplied by an integral of the positive parts of acceleration overa trip. Alternatively, instead of integration of an acceleration curveor function, acceleration could be summed in a piecewise manner, similarto the techniques described above with reference to Formulas (3) and(6). Acceleration data could be collected for example by an accelerationsensor carried by the vehicle, by determining change in velocity basedon data from a velocity sensor carried by the vehicle, as non-limitingexamples.

Similar to as discussed above with reference to FIG. 3 , the weightcoefficients α₁, α₂, α₃, α₄, and α₅ can be determined by least squaresregression, based on energy consumption data from a plurality of trips.

FIG. 7 is a schematic diagram of a system 700, including a telematicdevice 704 which communicatively couples to a vehicle by a port 702 ofthe vehicle. Telematic device 704 includes components which are, in theillustration, grouped logically into sensor interface components 710 andcontrol components 720. No physical or spatial grouping of thesecomponents is necessary, but rather the grouping discussed herein is alogical delineation for ease of discussion.

Sensor interface 710 is shown as including a communication interface 712configured to interface with matching port 702 in a vehicle. In anexemplary implementation, port 702 is a diagnostic port (such as anOBDII port) of the vehicle, and communication interface 712 is amatching diagnostic port plug (such as a plug which fits in an OBDIIport). Other forms and standards of ports and communication interfacesare possible, as appropriate for a given application. Data from thevehicle (such as sensor data from one or more sensors of the vehicle) isprovided to sensor interface 710 of telematic device 704 via port 702and communication interface 712. Vehicle sensors can include, asnon-limiting examples, a speed sensor, an inertial sensor, an RPMsensor, a battery temperature sensor, an ambient temperature sensor, abattery voltage sensor, a battery charge sensor, a location sensor, andany other appropriate sensors which collect vehicle-related data.

Sensor interface 710 is also shown as including at least one sensor 714.In the illustrated example, two sensors 714 are illustrated, but anyappropriate number of sensors could be included as appropriate for agiven application. Data pertinent to the vehicle can be collected bysensors such as sensor 714. In this way, data can be collected which isnot collected by sensors in the vehicle, or is not reported over anaccessible port such as port 702. Sensors 714 could include, asnon-limiting examples, a speed sensor, an inertial sensor, an ambienttemperature sensor, a location sensor, an image sensor (e.g. camera),and any other appropriate sensors which collect vehicle-related data.

Sensor interface 710 is also shown as including a communicationinterface 716, which communicates with a peripheral device 790.Peripheral device 790 includes at least one sensor 792, and can providedata collected by the at least one sensor 792 to telematics device 704via communication interface 716. In this way, data can be collectedwhich is not collected by sensors in the vehicle, is not reported overan accessible port such as port 702, or is not collected by sensors intelematic device 704. The at least one sensor 792 could include, asnon-limiting examples, a speed sensor, an inertial sensor, an ambienttemperature sensor, a location sensor, an image sensor (e.g. camera),and any other appropriate sensors which collect vehicle-related data.

Optionally, peripheral device 790 can also include at least oneprocessor 794 and at least one processor 796. Peripheral device 790 canthus be used to performs acts of the methods discussed herein (by the atleast one processor 792 executing processor-executable instructionsstored at the at least one non-transitory processor-readable storagemedium 796).

Communication interface 712 (and port 702), sensors 714, andcommunication interface 716 (and sensor 792) show multiple means bywhich telematics device 704 can collect sensor data. However, each ofthese components is not necessarily required. For example, any ofcommunication interface 712, sensors 714, or communication interface 716can be omitted, as long as one means of collecting sensor data remains.

Telematic device 704 (optionally in combination with peripheral device790) can be implemented, for example, as any of vehicle devices 122 inFIG. 1 . Telematic device 704 (optionally in combination with peripheraldevice 790) can also be used in the context of any of the methodsdiscussed herein (in particular, method 200 in Figure and/or method 400in FIG. 4 ).

As an example with reference to method 200 in FIG. 2 , acts 202, 204,206, 208, and 210 of method 200 can be performed by the at least oneprocessor 722, by executing processor-executable instructions stored atnon-transitory processor-readable storage medium 724. Further, prior tomethod 200, the telematic monitoring device can collect the trip data bycollecting sensor data. In some implementations, collecting the sensordata comprises collecting the sensor data from at least one sensorexternal to the telematic device 704 (e.g. from at least one vehiclesensor, via port 702 and communication interface 712; or from at leastone sensor 792 of peripheral device 790, by communication interface716). In some implementations, collecting the sensor data comprisescollecting the sensor data from at least one sensor included in thetelematic device 704 (e.g. at least one of sensors 714). In someimplementations, collecting the sensor data comprises collecting thesensor data from at least one sensor external to the telematic device704, and from at least one sensor included in the telematic device 704.

FIG. 8 is a schematic diagram which illustrates an exemplary display800. Display 800 presents a graphical user interface displayed to auser, which shows a map area 810 and energy information area 820. Theinterface areas illustrated in FIG. 8 are exemplary, and interfacesareas could be removed, shifted, reorganized, or added as appropriatefor a given application. Further, interface areas do not need to bepermanent. For example, display 800 can be a general-purpose display,where the content and areas displayed change depending on use. In someimplementations, display 800 is part of an entertainment display,navigation display, information display, “infotainment” display, tablet,smartphone, PDA, or any other appropriate device positioned at avehicle. The device can be permanently affixed at the vehicle (e.g.built in, or permanently mounted as an aftermarket component), or can bea portable device removable from the vehicle (e.g. a device carried by adriver of the vehicle).

FIG. 8 also shows at least one processor 892 and at least onenon-transitory processor-readable storage medium 894 communicativelycoupled to display 800. In some implementations, display 800, processor892, and non-transitory processor-readable storage medium 894 arepackaged as a single device (e.g. a navigation device, a smartphone, aninfotainment device, etc.). In other implementations, display 800,processor 892, and non-transitory processor-readable storage medium 894can be packaged separately (e.g. display 800 could display content asinstructed by a processor of another device, such as a telematicsdevice, an onboard vehicle computer, etc.).

The elements of FIG. 8 can be used in the context of the methodsdescribed herein. With reference to method 200 in FIG. 2 , the trip datacan be generated based on simulation. In the example if FIG. 8 , a tripis simulated between origin 812 and destination 814, shown according toroute 816. The origin 812 and destination 814 could be input by a user,or could be automatically retrieved as appropriate. For example, origin812 could be retrieved as corresponding to a present location of avehicle as indicated in location data from a location sensor of thevehicle. As another example, destination 814 could be automaticallyretrieved based on a driver history or schedule (e.g. the destinationcan be set as the driver’s home when a present time is close to a timethe driver normally heads home, or the destination 814 could beretrieved from a delivery scheduling service indicating a next stop forthe driver, as non-limiting examples). Note that while display 800 showsa visualization of a simulated trip, such visualization is notnecessary. A trip can be simulated without outputting said simulation toa vehicle driver (e.g. simulating a trip or plurality of trips of avehicle based on a travel schedule for the vehicle).

The trip between origin 812 and destination 814 is simulated (e.g. bythe at least one processor 892), and trip data is generated for thesimulated trip. For example, a sequence of geographic positions can begenerated representing route 816. As another example, timestamps can begenerated for the trip based on expected speed of the vehicle over thetrip (e.g. based on speed limit data, weather data, or any otherappropriate data). As yet another example, ambient temperature of theenvironment can generated, for example by retrieving temperature datafrom a weather database. As yet another example, speed data can begenerated (e.g. based on speed limit data, historical traffic data, orany other appropriate type of data).

Based on the generated trip data, method 200 can be performed, therebyproviding an estimated energy consumption by the vehicle for the trip.This estimated energy consumption is presented to the user via display800 in Energy information area 820 (11 kWh, in the illustrated example).Display 800 also shows an amount of energy available in the vehiclebattery in Energy information area 820 (20 kWh in the illustratedexample). Amount of energy available could be identified based on astate of charge indication from the vehicle (e.g. received over port702), or by determining battery level based on a measured voltage of thebattery, as non-limiting examples.

In the illustrated example, feasibility of the trip is also indicated inEnergy information area 820 (One way: Possible, in the illustratedexample). Feasibility can be determined by comparing the estimatedenergy consumption for the trip to energy available. When the estimatedenergy consumption is less than available energy, the trip should bepossible. However, a safety threshold could also be considered, wherethe available energy should exceed the estimated energy consumption by acertain amount in order for the trip to definitively be indicated aspossible, in the event that the estimated energy consumption is below anactual energy consumption for the trip.

In the illustrated example, feasibility of a return trip is alsoindicated in Energy information area 820 (Return Journey: At Risk, inthe illustrated example). In some implementations, feasibility of thereturn journey is evaluated by assuming the same energy consumption ofthe trip as previously estimated. In other implementations, the returnjourney can be separately simulated to generate trip data (similar to asperformed for the outgoing journey), and estimated energy consumptiondetermined in accordance with method 200. Such an implementation willtypically be more accurate, as differences between the outgoing journeyand the return journey can be accounted for (e.g. different levels oftraffic, different weather, different amounts of road incline).

Display of possibility of the return journey is not always relevant, andis thus an optional feature in the exemplary implementation. As andexample, a vehicle does not always need to make a return journey alongthe same route. As another example, a charge station may be availablefor the vehicle to replenish energy at the destination.

The exemplary implementation discussed with reference to FIG. 8 ismerely exemplary, and achieves similar objectives to those of method 400in FIG. 4 . In this regard, description of method 400 is applicable toFIG. 8 unless context dictates otherwise, and likewise description ofFIG. 8 is applicable to method 400 unless context dictates otherwise.

In some implementations, the energy consumption as determined in act 210of method 200 is output to a management device. In an example, withreference to FIG. 1 , a vehicle device 122 determines energy consumptionfor a trip, then outputs the determined energy consumption to managementdevice 110. In this example, management device 110 manages a pluralityof vehicles which operate out of a particular location, lot, orwarehouse. The management device receives estimated energy consumptionfor at least one upcoming trip for each vehicle of a plurality ofvehicles, as well as state of charge data (data indicating level ofcharge) for the plurality of vehicles. Based on estimated energyconsumption for the at least one trip, charging of the plurality ofvehicles (by a limited number of charge stations) can be prioritized sothat each vehicle has sufficient energy to complete it’s upcoming atleast one trip.

In some implementations similar to the above implementation, themanagement device estimates energy consumption for at least one upcomingtrip for a plurality of vehicles managed by the management device(instead of receiving estimated energy consumption as determined byvehicle devices at the vehicles). The management device receives stateof charge data (data indicating level of charge) for the plurality ofvehicles. Based on estimated energy consumption for the at least onetrip, charging of the plurality of vehicles (by a limited number ofcharge stations) can be prioritized so that each vehicle has sufficientenergy to complete its upcoming at least one trip.

In an illustrative example (applicable in the two precedingimplementations), a fleet of vehicles operates during the day, andreturns to a depot at night for charging. Energy consumption for eachvehicle for the next day (i.e. energy consumption for at least one tripexpected to be performed by the vehicle the next day) is estimated inaccordance with method 200 (either by vehicles devices 122 or bymanagement device 110). Vehicles with a state of charge furthest from aneeded energy the next day are prioritized for charging. Once charged(either to full charge or to a sufficient charge for the next day),these vehicles can be jockeyed so that other vehicles can charge (i.e.the vehicle coupled to a charge station is swapped).

In the above implementations and examples, management device 110 isdescribed as managing the vehicles. However, in alternativeimplementations, device 130 can manage vehicles in a similar way.Further, the management devices are not limited to managing a singlelocation, but can manage a fleet of vehicles across multiple locations.

In some implementations, energy consumption for historical trips by atleast one vehicle can be estimated in accordance with method 200, evenif the at least one vehicle is not an electric or battery-poweredvehicle (similarly to as discussed earlier). For example, energyconsumption for historical trips by at least one vehicle can beestimated in accordance with method 200 (e.g. by a management devicesuch as device 110 or device 130 in FIG. 1 ), to evaluate feasibility ofimplementing electric or battery-powered vehicles in place of theexisting at least one vehicle. In an example, estimated energyconsumption by the at least one vehicle can be compared to energycapacities of battery-powered vehicles available for purchase. Estimatedenergy consumption for a trip (or an accumulation of estimated energyconsumption over a plurality of trips), between rest periods where thevehicle can charge, can be compared to amount of energy replenishable tothe vehicle during charge periods. If sufficient energy is replenishableduring rest periods where the vehicle can charge, such that the vehicleis able to perform the trip (or plurality of trips) for which energyconsumption is estimated, the vehicle is deemed suitable for replacementby a battery-powered vehicle. Determination of suitability for vehiclesas battery-powered vehicles is described in U.S. Provisional Pat.Application No. 63/389,560 and U.S. Non-Provisional Pat. Application No.17/981,614, the entirety of which are incorporated by reference herein.

While the present invention has been described with respect to thenon-limiting embodiments, it is to be understood that the invention isnot limited to the disclosed embodiments. Persons skilled in the artunderstand that the disclosed invention is intended to cover variousmodifications and equivalent arrangements included within the scope ofthe appended claims. Thus, the present invention should not be limitedby any of the described embodiments.

Throughout this specification and the appended claims, infinitive verbforms are often used, such as “to operate” or “to couple”. Unlesscontext dictates otherwise, such infinitive verb forms are used in anopen and inclusive manner, such as “to at least operate” or “to at leastcouple”.

The specification includes various implementations in the form of blockdiagrams, schematics, and flowcharts. A person of skill in the art willappreciate that any function or operation within such block diagrams,schematics, and flowcharts can be implemented by a wide range ofhardware, software, firmware, or combination thereof. As non-limitingexamples, the various embodiments herein can be implemented in one ormore of: application-specific integrated circuits (ASICs), standardintegrated circuits (ICs), programmable logic devices (PLDs),field-programmable gate arrays (FPGAs), computer programs executed byany number of computers or processors, programs executed by one or morecontrol units or processor units, firmware, or any combination thereof.

The disclosure includes descriptions of several processors. Saidprocessor can be implemented as any hardware capable of processing data,such as application-specific integrated circuits (ASICs), standardintegrated circuits (ICs), programmable logic devices (PLDs),field-programmable gate arrays (FPGAs), logic circuits, or any otherappropriate hardware. The disclosure also includes descriptions ofseveral non-transitory processor-readable storage mediums. Saidnon-transitory processor-readable storage mediums can be implemented asany hardware capable of storing data, such as magnetic drives, flashdrives, RAM, or any other appropriate data storage hardware.

What is claimed is:
 1. A method for estimating energy consumption by avehicle for a trip by the vehicle, based on trip data representative ofthe trip, the method comprising: identifying a distance of travel forthe trip based on a plurality of geographic positions represented in thetrip data; identifying a duration of the trip based on a plurality oftimestamps in the trip data; identifying an ambient temperature of anenvironment of the vehicle for the trip based on temperature data;identifying speed of the vehicle for the trip based on the trip data;determining energy consumption by the vehicle for the trip as a weightedsum of: energy loss due to vehicle friction, based on the identifieddistance of travel; a total time of the trip, based on the identifiedtotal time of the trip; energy loss due to temperature control of thevehicle, based on a difference between the identified ambienttemperature of the vehicle and an optimal temperature, for the durationof the trip; and energy loss due to air resistance, based on theidentified speed of the vehicle; and outputting the determined energyconsumption by the vehicle for the trip.
 2. The method of claim 1,wherein identifying the distance of travel comprises: determiningrespective distances between sequential geographic positions representedin the trip data; summing each of the determined respective distances.3. The method of claim 1, wherein identifying a duration of the tripcomprises determining a difference between a first timestampcorresponding to a beginning of the trip and a second timestampcorresponding to an end of the trip.
 4. The method of claim 1, wherein:the temperature data is separate from the trip data; and identifying anambient temperature of an environment of the vehicle comprisesidentifying, from the temperature data, ambient temperature during thetrip for a geographical region corresponding to a geographical regionwhere the trip occurred.
 5. The method of claim 1, wherein: thetemperature data is included in the trip data, and the temperature dataincludes a plurality of indications of temperature of an environment ofthe vehicle during the trip; the method further comprises determining anaverage ambient temperature of an environment of the vehicle over thetrip, by averaging temperature indicated in the plurality of indicationsof temperature of an environment of the vehicle during the trip; andenergy loss due to temperature control of the vehicle is based on adifference between the determined average ambient temperature and anoptimal temperature, for the duration of the trip.
 6. The method ofclaim 1, wherein: the temperature data is included in the trip data, andthe temperature data includes a plurality of indications of temperatureof an environment of the vehicle during the trip; and energy loss due totemperature control of the vehicle is based on a respective differencebetween each indication of ambient temperature of the vehicle and anoptimal temperature, over the duration of the trip.
 7. The method ofclaim 1, wherein: identifying a speed of the vehicle comprisesidentifying speed of the vehicle for a plurality of segments of thetrip; and energy loss due to air resistance for the trip is determinedas a summation of energy loss due to air resistance for each segment ofthe trip, based on the identified speed of the vehicle for each segmentof the trip.
 8. The method of claim 7, wherein identifying speed of thevehicle for a plurality of segments of the trip comprises: identifyingeach segment of the trip as being between sequential geographicpositions indicated in the trip data with corresponding timestamps; andidentifying speed of the vehicle for each segment of the trip bydetermining, for each segment of the trip, distance of the segment asdistance between sequential geographic positions corresponding to therespective segment, duration of the segment as difference in timebetween timestamps corresponding to a beginning of the segment and anend of the segment, and dividing the respective distance by therespective duration for the segment.
 9. The method of claim 1, whereinthe weighted sum further includes energy consumed to impart kineticenergy to the vehicle, based on change in speed of the vehicle duringthe trip.
 10. The method of claim 9, wherein the weighted sum furtherincludes energy recovered by a regenerative braking system of thevehicle, based on change in speed of the vehicle during the trip. 11.The method of claim 1, further comprising: collecting, by a telematicmonitoring device positioned at the vehicle, the trip data.
 12. Themethod of claim 1, further comprising: simulating the trip by thevehicle; and generating the trip data based on the trip as simulated.13. The method of claim 1, wherein outputting the determined energyconsumption by the vehicle for the trip comprises presenting thedetermined energy consumption by the vehicle for the trip to a user ofthe vehicle by a user interface positioned at the vehicle.
 14. Themethod of claim 1, wherein outputting the determined energy consumptionby the vehicle for the trip comprises presenting the determined energyconsumption by the vehicle for the trip to a manager of the vehicle by avehicle management device external to the vehicle.